Blockchain Papers

Anju Devi, Geetanjali Rathee, Hemraj Saini

No abstract is available for this record.

Alfredo J. Pérez, Sherali Zeadally

No abstract is available for this record.

Alexander Nubbaum, Johannes Schütte, Luoyao Hao, Henning Schulzrinne · 5 authors

Since the monitoring of environmental emissions is mostly in the hands of regulatory authorities, collected data may not be easily observed by the interested public. Centrally stored data may also tempt the authorities or others to manipulate the historical record for political or liability reasons. To enable timely, transparent and integrity-protected collection and presentation of emission data, we propose and implement Tremble, an emission monitoring system based on blockchain and IoT sensors. Tremble employs a hybrid storage approach to lower the cost of storage compared to using a pure blockchain without losing data integrity. It provides web interfaces and visualizations for end users to query emission values they are concerned about. Qualitative and quantitative studies involving a total of 62 subjects demonstrate the usability of the system.

Qian Zhang, Sheng Cao, Xiaosong Zhang

Nowadays, online medical services have been greatly developing. Cryptocurrencies like Bitcoin and Ethereum are very suitable for online medical electronic payment scenarios that require identity privacy protection because of their good anonymity and financial payment attributes. However, cryptocurrencies varies widely, the need of cryptocurrencies exchange is urgent for patients to pay different doctors and platforms with diverse cryptocurrencies. Exchanging cryptocurrencies through centralized exchanges has problems such as high fees and cumbersome operations. The decentralized exchanges mainly focus on cross-blockchain connectivity but high intermediate fees charged by connectors are ignored. In order to minimize the exchange fees, we propose a cross-blockchain connector selection scheme utilizing the reverse Vickrey auction along with Interledger. Our scheme abstracts the connector nodes selection into a service provider bidding process, throught which we can find the very node with the lowest bid, namely, the least exchange fees, as the ideal cross-blockchain service provider. Our scheme implements cross-blockchain payment of different cryptocurrencies conveniently, quickly and cheaply, which can provide patients with better identity protection of personal privacy information. Security analysis and performance evaluations show that our scheme can effectively promote the applications of cryptocurrencies in the field of medical care.

Riya Tapwal, Pallav Kumar Deb, Sudip Misra, Surjya K. Pal

In this article, we propose a dynamic-consensus-based blockchain system—A-Blocks—for efficiently managing the data produced by the sensors in an Industrial Internet of Things (IIoT) environment. Typically, industries deal with a heterogeneous set of data from a diverse range of sensors. Conventional blockchain adoptions are a popular choice in such scenarios for data security while satisfying both transparency and immutability. However, stringent consensus algorithms are inadequate for managing heterogeneous data, especially due to its implicit constraints. For instance, while PoW provides inevitable security and is highly distributive, it is not scalable and requires more energy. In contrast, PoS is energy efficient but has reduced scalability and PBFT is suitable for faster processing. A-Blocks exploits the features of the available consensus algorithms and dynamically selects the best one in real time. It operates in two phases: 1) categorizing the data into groups based on their traits and then 2) selecting the appropriate consensus algorithm. Extensive experimental results using open industrial data sets demonstrate the effectiveness of A-Blocks with 8% CPU and 78% memory consumptions on resource-constrained devices. Furthermore, compared to the existing methods, although A-Blocks increases energy consumption by 11%, it also reduces mining time by 7%.

Rifat Sönmez, Ferda Özdemir Sönmez, Salar Ahmadisheykhsarmast

No abstract is available for this record.

Jiaxing Wang, Lanlan Rui, Yang Yang, Miaomiao Wang · 6 authors

No abstract is available for this record.

Qin Hu, Zhilin Wang, Minghui Xu, Xiuzhen Cheng

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.

Tao Peng, Kejian Guan, Jierong Liu, Jianer Chen · 6 authors

Abstract With the popularity and development of sensors‐containing intelligent terminals, mobile crowdsensing system (MCS) based on the Internet of Things (IoT) has become a new paradigm of application. By the MCS, the pervasive smart device users are enabled to collect large‐scale data cost‐effectively, for crowd intelligent extraction and human‐centric service delivery. However, most of the existing MCSs are based on a centralized structure vulnerable to attacks and intrusions. Moreover, the data collected through crowdsensing are diverse and difficult to guarantee user privacy, especially during the payment and data upload stages. In this article, we propose a blockchain‐based privacy‐preserving crowdsensing (BPPC) scheme based on the distributed structure, to protect user privacy. First, we combine the multiblockchain technology and K‐anonymity to construct anonymity groups for the confusion. Second, we present the random algorithm HashProof to select candidates from the anonymity groups to avoid deployment of Trusted Third Party (TTP) or agent server. Ultimately, we design encryption‐based algorithms building trust and authentication mechanisms in the system to guarantee the confidentiality of user data and achieve the accurate distribution of rewards. To verify the effectiveness and efficiency of the BPPC scheme, extensive experiments were conducted.

Shasha Li, Xiaodong Bai, Songjie Wei

Crowdsourcing relies on Internet-wide capability to solve the complicated or large-scale tasks that are difficult to accomplish separately by individuals. However, traditional centralized crowdsourcing systems highly depend on the centralized coordination server to operate, making it extremely vulnerable to the single-point bottleneck and failure. And the whole system lacks verifiable trustworthiness among the participants. This paper proposes a blockchain-based framework for the distributed crowdsourcing without relying solely on any single trusted entity. The solutions for the outsourced tasks are verified with consensus among the participants with a reputation mechanism. We prove by theoretical security analysis that the proposed scheme resists malicious attacks better comparing to other typical crowdsourcing schemes. A prototype system is implemented based on Ethereum to demonstrate the overhead performance in various aspects. Theoretical and experimental evaluations show that the proposed scheme possesses reliability, security, quality, and feasibility.

Kuo Chuen LEE David, Joseph J. Lim, Kok Fai Phoon, Yu Wang

Consensus is a key concept in Blockchain and Distributed Ledger Technologies. Blockchain networks are distributed in a manner whereby participants do not necessarily know or trust each other. This makes agreeing on the state of the network challenging, particularly when there may be bad actors. Consensus protocols are designed to achieve agreements within the network and to ensure that participants act in the best interest of the network.

Zhiyuan Huang, Jun Zheng, Mingjun Xiao

Crowdsourcing Data Trading is a novel paradigm, where a platform can aggregate data collected by a group of mobile users. Traditional crowdsourcing data trading systems rely on trusted data trading brokers, which increase costs and cannot prevent collusion. What’s more, they don’t take of data quality and truthfulness into consideration con-currently. The emergence of blockchain and smart contracts brings in a decentralized scheme. However, the selection of the most effective providers remains challenging. To tackle these problems, we propose a dynamic-game-with-complete-information-and-Blockchain-based Crowdsourcing data Trading system (CBCT), which mainly includes a smart contracts called CBCToken. Firstly, we replace the data trading broker and adopt the Stackelberg Game, a Dynamic Game with complete information (DGC), to manage the selection of providers. Moreover, homomorphic watermarking technology is applied to protect the data copyright. Lastly, we deploy BCDToken on an Ethereum test network to demonstrate its practicability and significant performances.

Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan C. Burguillo · 6 authors

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

Raunak Sarbajna, Christoph F. Eick, Áron Lászka

For first responders entering into a post-disaster situation, there is usually a severe lack of up-to-date ground truth. The initial period of time has multiple sources of conflicting information coming in and creating confusion about the situation. The most important immediate requirement is to create a traversal map, highlighting navigable paths to victims of the disaster and possible hazardous locations. Due to infrastructure damage, it is hard for existing centralized geospatial portals to quickly update and provide this information, which has become outdated. IoT solutions that can be deployed without extensive preparation provide the capability to quickly acquire and disseminate essential information to rescue teams. In this paper, we present a decentralized system, named DEIMOSBC, that is able to provide such a mapping service faster and more reliably, utilizing the work of volunteers and relying on a blockchain backend that is based on an IoT system. Our solution utilizes the availability of modern smartphones with GPS receivers and processing capabilities to collect sequences of GPS locations and chain them into trajectories. These trajectory data are submitted as entries into a blockchain after cleaning them through a purpose-built smart contract. DEIMOSBC relies on the inherent robustness and distributed nature of a blockchain to make collating and assembling a map from these paths more accurate and less susceptible to disruption. We describe how DEIMOSBC would work for a hypothetical disaster scenario of a Category 5 hurricane striking an area of the Gulf of Mexico.

Hao‐Tian Wu, Yucong Zheng, Bowen Zhao, Jiankun Hu

In mobile crowdsensing (MCS), sensing data uploaded by dishonest workers may be false or even malicious. Thus, a reputation management system is often set up by using workers’ historical behaviors to indicate the quality of sensing data. As existing management schemes usually protect the reputation update process, reputation scores are generally stored in plaintext, which may destroy the fair bidding property of an MCS system. To address this issue, we propose an anonymous reputation management system based on the dual blockchain architecture, where reputation scores are masked. More precisely, one chain is used to store and update reputation scores, and another chain is responsible for publishing tasks and storing task-related data. To anonymously update and verify the reputation scores without affecting their usages in data sensing process, a kind of ring signature and Pedersen commitment is employed in smart contracts. In addition, a Schnorr signature is generated to make the reputation scores verifiable in the MCS system. We implement a prototype system on Hyperledger Fabric, and simulation results are provided for comparisons with two existing schemes.

Xuyang Ma, Du Xu, Katinka Wolter

No abstract is available for this record.

Vaikkunth Mugunthan, Ravi Rahman, Lalana Kagal

No abstract is available for this record.

Huajian Wang, Huan Zhou, Yang Guogui, Tao Xiao

Crowdsourcing provides a new way of group intelligence interaction in recent years. Traditional crowdsourcing service models rely on centralized third-party platforms, which are bottlenecks in credibility. Blockchain is a potential solution. We therefore propose DCrowd, a Decentralized and Credible crowdsourcing model based on game theory and smart contracts. The workers in DCrowd are organized in a decentralized manner. However, information on the blockchain is open, which may cause data leakage and privacy issues. To tackle the data transparency issue, a commitment scheme is leveraged for data submission among workers. Then, an unbiased random selection algorithm is further designed to select independent workers from the dispersed worker pool to avoid possible collusion. Through the Nash equilibrium principle, it is proved that workers in DCrowd have to perform honestly to maximize their rewards. Finally, the feasibility of our model design is demonstrated through experiments on Ethereum.

Ponlawat Weerapanpisit, Sergio Trilles, Joaquı́n Huerta, Marco Paìnho

Social Internet of Things (SIoT) is a concept that integrates the Internet of Things and human social networks. An SIoT system has to store and manage device reputation values, which are used by end devices to determine the trustworthiness of another one. This device trustworthiness can also be affected by its geographical location. In this work, we introduced an architecture that includes the geospatial context in the part concerned with reputation management. The proposed architecture is based on the cloud-fog-edge architecture and uses the fog layer as the management system. The devices in the fog layer form an Ethereum Blockchain network and store the Smart Contracts. These in turn allow the management functionalities to be carried out in a decentralised, transparent and secure way, which are the advantages of Blockchain. To enable the characteristics with a geospatial component, it is necessary to apply a geocoding technique. This work shows how geocoding techniques can be adapted to cover the main geospatial functionalities and compares two geocoding options (Geohash or S2). The results showed that it is possible to include the geospatial context in a decentralised reputation management system by using hierarchical geocoding techniques, and the experiments showed that both Geohash and S2 can offer a similar performance in the proposed architecture.

Chunxiao Li, Xidi Qu, Yu Guo

Abstract Blockchain technology has attracted considerable attention due to the boom of cryptocurrencies and decentralized applications. Among them, the emerging blockchain-based crowdsourcing is a typical paradigm, which gets rid of centralized cloud-servers and leverages smart contracts to realize task recommendation and reward distribution. However, there are still two critical issues yet to be solved urgently. First, malicious evaluation from crowdsourcing requesters will result in honest workers not getting the rewards they deserve even if they have provided valuable solutions. Second, unfair evaluation and reward distribution can lead to low enthusiasm for work. Therefore, the above problems will seriously hinder the development of blockchain-based crowdsourcing platforms. In this paper, we propose a new blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness, named TFCrowd. The core idea of TFCrowd is utilizing a smart contract of blockchain as a trusted authority to fairly evaluate contributions and allocate rewards. To this end, we devise a reputation-based evaluation mechanism to punish the requester who behaves as “false-reporting” and a Shapley value -based method to distribute rewards fairly. By using our proposed schemes, TFCrowd can prevent malicious requesters from making unfair comments and reward honest workers according to their contributions. Extensive simulations and the experiment results demonstrate that TFCrowd can protect the interests of workers and distribute rewards fairly.

Yilong Hui, Yuanhao Huang, Zhou Su, Tom H. Luan · 7 authors

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.

Jianxiong Guo, Xingjian Ding, Tian Wang, Weijia Jia

With the continuous expansion of Internet of Things (IoT) devices, edge computing mode has emerged in recent years to overcome the shortcomings of traditional cloud computing mode, such as high delay, network congestion, and large resource consumption. Thus, edge-thing systems will replace the classic cloud-thing/cloud-edge-thing systems and become mainstream gradually, where IoT devices can offload their tasks to neighboring edge nodes. A common problem is how to utilize edge computing resources. For the sake of fairness, double auction can be used in the edge-thing system to achieve an effective resource allocation and pricing mechanism. Due to the lack of third-party management agencies and mutual distrust between nodes, in our edge-thing systems, we introduce blockchains to prevent malicious nodes from tampering with transaction records and smart contracts to act as an auctioneer to realize resources auction. Since the auction results stored in this blockchain-based system are transparent, they are threatened with inference attacks. Thus in this paper, we design a differentially private combinatorial double auction mechanism by exploring the exponential mechanism such that maximizing the revenue of edge computing platform, in which each IoT device requests a resource bundle and edge nodes compete with each other to provide resources. It can not only guarantee approximate truthfulness and high revenue, but also ensure privacy security. Through necessary theoretical analysis and numerical simulations, the effectiveness of our proposed mechanisms can be validated.

Sheng Gao, Xiuhua Chen, Jianming Zhu, Xuewen Dong · 5 authors

Worker selection in crowdsensing plays an important role in the quality control of sensing services. The majority of existing studies on worker selection were largely dependent on a trusted centralized server, which might suffer from single point of failure, the lack of transparency and so on. Some works recently proposed blockchain-based crowdsensing, which utilized reputation values stored on blockchains to select trusted workers. However, the transparency of blockchains enables attackers to effectively infer private information about workers by the disclosure of their reputation values. In this article, we proposed the TrustWorker, a trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing. By taking the advantages of blockchains such as decentralization, transparency and immutability, our TrustWorker could make the worker selection process trustworthy. To protect workers’ reputation privacy in our TrustWorker, we adopted a deterministic encryption algorithm to encrypt reputation values and then selected the top$N$workers in the light of secret minimum heapsort scheme. Finally, we theoretically analyzed the effectiveness and efficiency of our TrustWorker, and then conducted a series of experiments. The theoretical analysis and experiment results demonstrate that our TrustWorker can achieve trustworthy worker selection, while ensuring the workers’ privacy and the high quality of sensing services.

Haiqin Wu, Boris Düdder, Liangmin Wang, Shipu Sun · 5 authors

The ubiquity of crowdsourcing has reshaped the static sensor-enabled data sensing paradigm with cost efficiency and flexibility. Still, most existing triangular crowdsourcing systems only work under the centralized trust assumption and suffer from various attacks mounted by malicious users. Although incorporating the emerging blockchain technology into crowdsourcing provides a possibility to mitigate some of the issues, how to concretely implement the crucial components and their functionalities in a verifiable and privacy-aware manner remains unaddressed. In this article, we present BRPC, a blockchain-based decentralized system for general crowdsourcing. BRPC integrates the confident-aware truth discovery algorithm to provide task requesters with reliable task truths while evaluating each worker’s data quality. To mitigate the biased evaluation of malicious requesters, we propose a privacy-aware verification protocol leveraging the threshold Paillier cryptosystem, with which a certain number of workers can collaboratively verify the evaluation results without knowing any sensory data. Furthermore, we define the three roles of a user and elaborate a comprehensive reputation evaluation model enforced by smart contracts for its trustworthy running. Financial and social incentives are both offered to motivate users’ honest participation. Finally, we implement a prototype of BRPC and deploy it on the Ethereum blockchain. Theoretical analyses and experiment results show its security and practicality.